System and method for remote nitrogen monitoring and prescription

ABSTRACT

A method and system for managing nitrogen applied by nitrogen application equipment to a geographic region includes determining a growth stage for the geographic region using a crop module, and determining a nitrogen change for the geographic region based on the growth stage using a nitrogen change module, which can additionally or alternatively include determining an amount of nitrogen initially available for a geographic region.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/039,170, filed 18 Jul. 2018, which is a continuation of U.S.application Ser. No. 15/483,062, filed 10 Apr. 2017, which is acontinuation of U.S. application Ser. No. 15/345,027, filed 7 Nov. 2016,which claims the benefit of U.S. Provisional Application No. 62/252,102filed 6 Nov. 2015, both of which are incorporated in their entirety bythis reference.

U.S. application Ser. No. 15/345,027, filed 7 Nov. 2016, which is also acontinuation-in-part of U.S. application Ser. No. 14/929,055, filed 30Oct. 2015, which claims priority to U.S. Provisional Application No.62/072,911, filed 30 Oct. 2014, and is a continuation-in-part of U.S.application Ser. No. 15/012,762, filed 1 Feb. 2016, which claimspriority to U.S. Provisional Application No. 62/109,888, filed on 30Jan. 2015 and U.S. Provisional Application No. 62/130,314, filed on 9Mar. 2015, and is a continuation-in-part of U.S. application Ser. No.15/012,749, filed 1 Feb. 2016, which claims priority to U.S. ProvisionalApplication No. 62/109,842 filed 30 Jan. 2015 and US ProvisionalApplication No. 62/154,936 filed 30 Apr. 2015, all of which areincorporated herein in their entireties by this reference.

TECHNICAL FIELD

This invention relates generally to the agricultural field, and morespecifically to a new and useful nitrogen monitoring and prescriptionsystem and method in the agricultural field.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A-1B are flowchart diagrams of variations of the method of remotenitrogen monitoring.

FIG. 2 is a flowchart diagram of a variation of the method of remotenitrogen monitoring.

FIG. 3 is a schematic diagram of a variation of the method of remotenitrogen monitoring.

FIG. 4 is a schematic diagram of a variation of the method of remotenitrogen monitoring.

FIG. 5 is a schematic diagram of determining an updated module in avariation of the method of remote nitrogen monitoring.

FIG. 6 is a schematic diagram of determining an updated module in avariation of the method of remote nitrogen monitoring.

FIG. 7 is a schematic representation of an example of a user interfacefor presenting nitrogen availability over time.

FIG. 8 is a schematic representation of an example of a nitrogenprescription.

FIGS. 9A and 9B are examples of a field before and after the nitrogenprescription was executed on the field, respectively.

FIG. 10 is a schematic representation of an example of a nitrogen statusanomaly notification.

FIG. 11 is a schematic diagram of a variation of the method of remotenitrogen monitoring.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview.

As shown in FIGS. 1-3, a method 100 for managing nitrogen within ageographic region includes: determining a growth stage for thegeographic region using a crop module Silo; and determining a nitrogenchange for the geographic region using a nitrogen change module S120.The method can additionally or alternatively include determining anamount of nitrogen initially available for a geographic region S122.

As shown in FIG. 1B, the method 100 can additionally or alternativelyinclude determining a nitrogen prescription for the geographic regionbased on the nitrogen change using a nitrogen prescription module S130;generating control instructions for agricultural equipment based on thenitrogen prescription S140; determining an updated module S150 (e.g., anupdated crop module S152, an updated nitrogen change module S152, anupdated nitrogen prescription module S154, etc.); identifying a nitrogenstatus anomaly based on the nitrogen change S160; presentingnitrogen-related information to a user account S170; and/or reducingnoise associated with remote imagery, module inputs (e.g., yield map),module outputs (e.g., nitrogen prescription), and/or any other suitabledata. Reducing noise can include applying any one or more of: abootstrap filter, registration noise reduction, particle filter, noisereduction heuristics, and/or any other processing operations.

The method 100 functions to determine the amount of nitrogen that islost or gained from the geographic region (e.g., field), geographicsub-region (e.g., region of the field), and/or any suitable region overtime. The method 100 can additionally or alternatively function todetermine a nitrogen prescription (e.g., for initial fertilizerapplication, side dressing, etc.), which can be used for generatingcontrol instructions for agricultural equipment to apply nitrogenaccording to the nitrogen prescription. The nitrogen applied to thefield or field sub-regions can function to augment the nitrogenremaining in the field to reach a target parameter, such as a targetyield, growth stage distribution, or other parameter.

Any portion of the method 100 can be implemented with any combination ofcomponents of the system, and/or any suitable components.

2. Benefits

Nitrogen is an essential nutrient for crop growth and development.Therefore, there has been a long-felt need in the agricultural field tomonitor the amount of nitrogen available to plants in the soilsurrounding the plant. This need is in direct tension with the economicneed to decrease treatment costs (e.g., fertilizer application costs)that arise from the low economic margins that can be gained fromcommodity products. However, nitrogen monitoring presents severalchallenges. First, due to the size of the fields, manual nitrogenmeasurements can be impractical and costly. Second, remote monitoring ofnitrogen can be difficult given the lack of visual manifestation of thenitrogen on the field surface. Third, soil nitrogen can be highly mobileand dependent upon a plurality of factors (e.g., wind, rain, ambienttemperature, microbial population, soil texture, crop growth stage,etc.), which can render computational modeling and/or predictiondifficult. Fourth, groundtruth data for nitrogen-influenced effects canbe rare and/or difficult to obtain.

In specific examples, the method 100 and/or system can confer severalbenefits over conventional methodologies used for determining nitrogenchange, generating nitrogen prescriptions, and/or implementing nitrogenprescriptions.

First, the technology can analyze a host of nitrogen-related variables(e.g., soil parameters, weather parameters, etc.) to determine nitrogenchange for plants in analysis zones (e.g., geographic sub-regions,geographic regions, etc.). For example, the technology can leverage cropstressor data and/or remote imagery (e.g., satellite imagery from whichvegetative performance can be extracted) to predict current and futuregrowth stages for the analysis zones, which can function to determinethe variance in a crop's nitrogen consumption, depending on growthstage. Nitrogen change can be used to develop nitrogen prescriptions foreach geographic sub-region of a field. Nitrogen prescriptions caninclude a nitrogen application rate, amount, timing, location, or anyother suitable nitrogen application parameter. The nitrogen prescriptioncan be prescribed to optimize yield (e.g., as shown in FIGS. 9A-9B) orto obtain any other suitable goal or target.

Second, the technology can provide technical solutions necessarilyrooted in computer technology (e.g., computationally calibratingmodules, such as the crop module, with specialized datasets in aniterative process) to overcome issues specifically arising with thecomputer technology (e.g., improving accuracy for predicting growthstages and/or optimal nitrogen prescriptions, improving predictionspeed, etc.). For example, a crop module (e.g., for determining growthstage for plants within an analysis zone) can be calibrated by comparingcrop parameter outputs (e.g., from the crop module) to indirectmeasurements of the crop parameters. In a specific example, the leafarea index (LAI), determined from a vegetative performance value (e.g.,wide dynamic range vegetative index), can be used as a validation value(e.g., used as ground truth for automated crop module calibration, suchas parameter determination, equation determination, weighting valuedetermination, etc.). However, the method can automatically generate anyother suitable set of training data for module calibration, or otherwiseupdate the modules.

Third, the technology can confer an improvement to the functioning ofthe nitrogen application equipment. Nitrogen prescriptions optimized forachieving crop goals can be used in generating control instructions forcontrolling agricultural equipment (e.g., equipment movement,orientation, nitrogen application timing, amount, rate, etc.) inapplying nitrogen.

Fourth, the technology can effect a transformation of an object to adifferent state or thing. Nitrogen application equipment and/or otheragricultural equipment can be activated and controlled according tocontrol instructions determined, for example, based on nitrogenprescriptions. Digital imagery of a physical field can be transformedinto physical nitrogen application to the physical field, which cansubsequently result in physical crop yields and harvests. Crop fieldscan be transformed (e.g., from a nitrogen status anomaly state to anon-anomaly state; from a insufficiently fertilized state to asufficiently fertilized state for reaching crop goals, etc.) in responseto the nitrogen applied by nitrogen application equipment followingcontrol instructions.

Fifth, the technology can leverage specialized, non-generic computingdevice, such as fertilizer application equipment, seeding machinery,harvesting equipment, and/or other agricultural equipment, in performingnon-generic functions, such as applying nitrogen according to agenerated nitrogen prescription by executing control instructions;collecting specialized datasets for generating, updating, and orexecuting modules such as the nitrogen prescription module, or otherfunctions.

The technology can, however, provide any other suitable benefit(s) inthe context of using non-generalized computer systems for determiningnitrogen change, generating nitrogen prescriptions, and/or implementingnitrogen prescriptions.

3. System.

The system can include a crop module and a nitrogen change module. Thesystem can additionally or alternatively include a total nitrogenmodule, nitrogen prescription module, management zone module, one ormore computing systems configured to generate, execute, and/or updatethe one or more modules, agricultural equipment operable according tocontrol instructions generated based on outputs of the one or moremodules, or any other suitable component. The system functions todetermine a nitrogen change over time (e.g., from a prior nitrogenapplication to a current time) for crops within a geographic region. Thesystem can additionally or alternatively function to apply nitrogen tocrops in accordance with one or more nitrogen prescriptions.

In a specific example of module use, nitrogen application information(e.g., treatment date, amount of nitrogen applied) is received from afarmer account at the total nitrogen module, where the total nitrogenmodule determines the amount of applied nitrogen available for eachanalysis zone (initial nitrogen amount). A set of historic remote imagesof the management region (e.g., 5 or more years of satellite images) isprocessed by the management zone module into a potential yield value foreach analysis zone for each of a set of timeframes (e.g., recurrenttimeframes, such as growth stages; non-recurrent timeframes, such asgrowing season times or calendar days; etc.), which cooperatively form ayield map depicting the relative potential of each analysis zone,compared to other analysis zones within the field. The initial nitrogenamount, potential yield, weather since nitrogen application (e.g.,rainfall, wind, ambient temperature, growing degree day values, etc.),and soil data for each analysis zone is fed to the crop module, whichestimates the growth stage for crops within each analysis zone. Thegrowth stage, weather, and soil data are then fed into the nitrogenchange module to determine the amount of nitrogen lost or gained fromthe analysis zone to date, where the initial nitrogen amount and amountof lost or gained nitrogen can be cooperatively used to determine theamount of nitrogen left within the analysis zone. A crop goal (e.g.,crop yield, crop uniformity, minimum crop size, etc.) can be used todetermine the amount of nitrogen needed to reach the crop goal (e.g.,based on the current crop parameters, determined by the crop module, anda desired crop parameter), where the amount of nitrogen needed to reachthe crop goal and the amount of nitrogen lost from (and/or amount ofnitrogen remaining within) the analysis zone can be cooperatively usedto determine the amount of supplemental nitrogen that should be appliedto the analysis zone. The crop goal can be received from the farmeraccount, automatically determined based on market factors, automaticallydetermined based on historic farmer preferences, or otherwisedetermined. The amount of supplemental nitrogen can be used with anitrogen prescription module in determining a nitrogen prescription foreach analysis zone (e.g., each geographic sub-region). However, themodules can be otherwise used.

System modules can include any of a: process-driven module (e.g.,equation based module; differential equation module), fuzzy networkmodule, clustering module, unsupervised machine learning module (e.g.,artificial neural network, association rule learning, hierarchicalclustering, cluster analysis, outlier detection), supervised learningmodule, semi-supervised learning module, deep learning module, and/orany other suitable module leveraging any other suitable machine learningmethod, probabilistic approach, heuristic approach, deterministicapproach, and/or any combination thereof. In an example, a system module(e.g., crop module) can include a process-driven differential equationmodule. In another example, the system module (e.g., a crop module) caninclude an artificial neural network including input neuronscorresponding to values of image elements from an image (e.g., pixels ofa received satellite image of a geographic region). However, the inputneurons can correspond to any other suitable set of factor values. Theinputs and/or features (e.g., parameters used in an equation, featuresused in a machine learning model, etc.) used in a module can bedetermined through a sensitivity analysis, received from other modules(e.g., outputs), received from a user account (e.g., from the farmer,from equipment associated with the user account, etc.), automaticallyretrieved (e.g., from an online database, received through asubscription to a data source, etc.), extracted from sampled sensorsignals (e.g., images, etc.), determined from a series of sensor signals(e.g., signal changes over time, signal patterns, etc.), and/orotherwise determined.

The modules are preferably universally used (e.g., the same models usedacross all user accounts, fields, and/or analysis zones), but canalternatively be specific to a cultivar, user account, field, analysiszone, or otherwise differ. Different instances of the method can beperformed concurrently (e.g., in parallel), asynchronously, or at anyother suitable time. Modules can be generated, executed, or calibratedevery time the method is performed (e.g., based on up-to-dateinformation), once, at a time interval (e.g., every day, week, month,etc.), every time a newly-received data value differs from a predicteddata value; and/or at any other suitable frequency. Inputs and/oroutputs of the modules can be associated with any suitable temporalindicator (e.g., daily data, averages over a period of time, etc.).Additionally, any suitable inputs for a module (e.g., crop module) canbe used as inputs for another module (e.g., nitrogen change module), andany suitable outputs of a module can be used as inputs for anothermodule. In an example, one or more modules and/or combination of modulesof the system can be a time series module (e.g., where the output of amodule at a first time can be used as an input to a same or differentmodule at a second time, etc.). In a specific example, as shown in FIG.11, an initial nitrogen availability in the soil can be adjusted by acalculated nitrogen uptake and soil gain/loss to obtain a total changein nitrogen in the soil for a first time, and the estimated nitrogen inthe soil at the first time (e.g., day 1) can be used as the initialnitrogen availability in the soil at a second time (e.g., day 2). Inanother specific example, soil release data for the year preceding thecurrent planting year is run through the nitrogen change module todetermine the initially available nitrogen. In another specific example,as shown in FIG. 11, the method 100 can include determining a growthstage at a first time for plants within the geographic region using acrop module (e.g., with input parameters derived from remote monitoringdata); determining a first nitrogen change for the geographic regionbased on the growth stage using a nitrogen change module; estimating afirst nitrogen availability for the geographic region at the first timebased on the first nitrogen change; repeating the preceding portions ofthe method 100 for a second nitrogen availability for the geographicregion at a second time based on a second nitrogen change and the firstnitrogen availability. In another specific example, the crop moduleand/or other suitable modules can be executed and/or updated at apredetermined time interval (e.g., daily) based on median weather for arepresentative year and historic weather for the growing season.However, repeating portions of the method 100 can be performed in anysuitable manner.

The method can be applied to a single crop type or cultivar (e.g., corn,soy, alfalfa, wheat, rice, sugarcane, etc.), multiple crop types orcultivars, or any other suitable number of crop types and/or cultivars.In one variation, a different instance of the method (includingdifferent modules, module variants, factors considered, or otherparameters) can be applied to each different crop type. The differentmethod instances can be updated based on data associated with therespective crop type, or can be updated based on data from other croptypes. In a second variation, the same instance of the method can beapplied to multiple crop types. However, different instances of themethod can be applied to any suitable number of crop types, related inany suitable manner, and otherwise updated.

The method can be applied to one or more analysis zones (e.g., field orfield sub-region). Each analysis zone can include one or more croptypes. In one variation, a different instance of the method (includingdifferent modules, module variants, factors considered, or otherparameters) can be applied to each different analysis zone. Thedifferent method instances can be updated based on data associated withthe respective analysis zone, or can be updated based on data from otheranalysis zones. In a second variation, the same instance of the methodcan be applied to multiple analysis zones. However, different instancesof the method can be applied to any suitable number of analysis zones,related in any suitable manner, and otherwise updated.

The crop module of the system functions to determine the growth stage ofthe crop. The growth stage can subsequently be used to determine cropparameters, such as LAI, nitrogen consumption, nitrogen fixation, or anyother suitable crop parameter. The growth stage or crop parameter valuescan be determined for: a reference time, a growth stage, an analysistime (e.g., past, current, or future), and/or for any other suitabletemporal duration. The growth stage or crop parameter values can bedetermined, in relation to one or more zones (e.g., analysis zone,management zone, geographic sub-region, geographic region, field, etc.):for the population of crops; for each crop; for a model crop; as anaverage growth stage; as an average or model growth stage; and/or forany other suitable set of crops.

Features and/or inputs used in generating, executing, and or updatingthe crop module (or any other suitable module) can be include any one ormore of: the planting date; crop cultivar; seed density or distribution(e.g., based on the seed rate, seed prescription, seeding equipmentcapabilities, etc.); prior crop growth stage; current crop growth stage(e.g., as received from a user, estimated from remote sensormeasurements, etc.); historic crop growth stage for this time of thegrowing season; planting data; growing degree day values and/or otherheat indexes; crop density coefficients (e.g., for specific hybrids);crop stressor data (e.g., weather parameters, soil parameters,topological parameters, historic vegetative performance values such ashistoric LAI, treatment history such as watering history and/orpesticide application history, disease history, pest history, cropanomaly history, etc.); remote monitoring data (e.g., date, time, solarangle, satellite angle, band values such as for blue, green, red, rededge, near infra-red, etc.); proxy map (e.g., yield proxy map); and/orany other suitable data that can influence crop growth. The plantingdata can include one or more of historic, current, and/or predicted:data, population (e.g., 33,000 seeds/acre), type (e.g., Po339AMXT,Pioneer, CRM: 99, Silk: CRM 95, GDUs to Silk: 1190, GDUs to Phy. Mat.:2350, etc.). Weather parameters can include recorded, predicted,extracted, and/or otherwise obtained based on weather data or any othersuitable data. The weather data can include one or more of:precipitation, min and/or max temperature, irradiance, sunrise time,sunset time, ambient temperature, soil temperature, frost, freeze, snow,tornadoes, rain, wind, or any other suitable weather data. Weatherparameters and/or other suitable parameters can be aggregate parameters(e.g., averages, medians, etc.) calculated over any time period (e.g.,median in-season rainfall for a preceding year, for a coming year, for arepresentative year determined from historic years, etc.). Soilparameters can be recorded, predicted, extracted, or otherwise obtainedbased on soil data or any other suitable data. Soil data can be by mapunit (e.g., polygon) and/or horizon (e.g., layer), and can include oneor more of: drain quality, pH, saturation or SAT, OC, SW, DUL, LL, soillayer composition, sand composition, silt composition, clay composition,organic matter composition, soil layer thickness, slope, wilting point,hydraulic conductivity, porosity, field capacity, initial soil water,soil bulk density, second stage evaporation coefficient for summer,cumulative evaporation before soil supply becomes limiting for summer,second stage evaporation coefficient for winter, cumulative evaporationbefore soil supply becomes limiting for winter, coefficient forcalculating unsaturated unsaturated water diffusivity, slope forcalculating unsaturated water diffusivity, runoff curve number for baresoil, maximum reduction in curve number due to surface cover, thresholdof surface cover above which there is no effect on curve number,saturated soil drainage coefficient, carbon to nitrogen ratio for root,root growth factor, carbon to nitrogen ratio for soil, soil organiccarbon content, fraction of biome pool carbon, fraction of inert poolcarbon, nitrate concentration of the soil on a dry weight basis, or anyother suitable soil data or parameter thereof. In an example, the set ofcrop module inputs can include: growing degree day values, nitrogenuptake, crop density coefficients for specific crop hybrids, and weatherparameters (e.g., increased rainfall, leading to more nitrogen loss). Inanother example, the crop module inputs can include: crop definingfactors (e.g., carbon dioxide, solar radiation, temperature, cultivarcharacteristics, etc.), growth limiting factors (e.g., water,nutrients), and growth reducing factors (e.g., weeds, pests, diseases,frost and extreme heat). However, any other suitable input can beconsidered. The data considered within the crop module is preferablydata extending from the seeding date to the most up-to-date data (e.g.,current data), but can alternatively be any other suitable set of dataassociated with any suitable temporal indicators. The parameters and/ordata used by the crop module for analysis can be associated with thesame time period (e.g., all characterize the analysis zone from May toOctober of the same year), be associated with different time periods, orbe associated with any other suitable time periods that are related inany suitable manner. For example, weather data is preferably up-to-date,but the soil data can be historic data (e.g., collected before thegrowing season began). The inputs are preferably received from the useraccount (e.g., from the farmer, from equipment associated with the useraccount, etc.), but can alternatively be automatically determined (e.g.,from an online database such as the Soil Survey Geography (SSURGO)database, received through a subscription to a data source, etc.), or beotherwise determined. In a specific example, the crop module canconsider a subset of the aforementioned factors. For example, the cropmodule (or any other suitable module) can consider only DUL and SAT whendetermining the LAI value for the analysis zone. In this example, thecrop module can include or feed outputs to a LAI determination module.The LAI determination module can consider the same or a different set offactors from those considered by the crop module. In this example,parameters of the LAI determination module (e.g., constants for the DULand SAT factors, etc.) can be iteratively calibrated using image-derivedLAI as the ground truth (e.g., where the image can be a remote image,such as satellite imagery or a RapidEye image, be a local image, or beany other image). However, the crop module can consider any suitable setof factors in determining the growth stage, LAI, or any other suitablecrop parameter.

The crop module can output: the current crop growth stage, the historiccrop growth stage, the current nitrogen requirement, the amount ofnitrogen consumed, the predicted LAI, any other crop phenotype parametervalues, and/or any other suitable information.

The crop module can be calibrated by a calibration module, where thecalibration module can access the crop module parameters and/orequations, the most recent remote monitoring datapoint for the analysiszone, historic remote monitoring datapoints for the analysis zone,historic weather data for the analysis zone (e.g., for the instantaneousgrowing season, before the growing season, etc.), soil data for theanalysis zone, user data for the analysis zone (e.g., information forthe instantaneous growing season, such as planting date, cultivar, etc.;user preferences, etc.), and/or any other suitable information for cropmodule calibration. In a variation, the calibration module uses ashuffled complex evolution metropolis algorithm, and considers twenty ormore parameters, but any suitable algorithm can be used.

The nitrogen change module of the system functions to determine thecumulative amount of nitrogen lost from the soil to: crops, nitrogenleaching (e.g., loss with soil water), denitrification, volatilization,soil erosion and runoff, and/or any other suitable loss channel. Thenitrogen change module can additionally or alternatively function toaccount for the amount of nitrogen gained in the soil, such as fromnitrogen release (e.g., by organic matter such as dead plant cells,microorganisms, mineralization, etc.). The nitrogen change module caninclude a nitrogen loss module (e.g., for outputting nitrogen loss fromthe soil such as from crop uptake, etc.), a nitrogen gain module (e.g.,for outputting nitrogen gained in the soil such as from soil release,etc.), and/or any other suitable nitrogen change parameter. Featuresand/or inputs used in generating, executing, and or updating thenitrogen change module can include any one or more of: crop growth stage(e.g., historic, current, predicted), crop stressor data (e.g., same ordistinct from crop stressor data used by other modules), weatherparameters, soil parameters (e.g., nitrogen soil release based on soiltypes, etc.), fertilizer parameters (e.g., application information,fertilizer types, etc.), terrain maps, adjacent analysis zones andrelationship to the respective analysis zone (e.g., where runoff from amore elevated adjacent zone can increase the amount of availablenitrogen in the instantaneous zone), and/or any other suitable data thatcan influence nitrogen change. The features and/or inputs can bedetermined (e.g., calculated, extracted, etc.) from user inputs (e.g.,equipment feeds, user entries), data feeds (e.g., remote images, weathermodel outputs, soil surveys, social media, markets, etc.), systemmodules (e.g., crop module, nitrogen loss module, nitrogen releasemodule, etc.), or from any other suitable data source. The nitrogenchange module can output the amount of nitrogen change (e.g., changesince a previous nitrogen application, change over the growing season todate, change over a defined time period, etc.), rate of nitrogen change,nitrogen change for different types of nitrogen fertilizer (e.g.,anhydrous ammonia, urea, urea-ammonium nitrate solutions, etc.),residual amount of nitrogen leftover from an initial nitrogenapplication (e.g., from a preceding year), an estimated amount ofavailable nitrogen for plants within a zone at a given time, and/orother suitable nitrogen change parameters. For example, the growth stage(e.g., for determining crop uptake of nitrogen) and crop stressor data(e.g., rainfall) for the analysis zone can be used as an input into thenitrogen loss module to output a nitrogen loss in the soil. In anotherexample, weather parameters, soil parameters, and fertilizer parameterscan be input into the nitrogen gain module to output an amount ofnitrogen gained in the soil (e.g., due to soil release). In anotherexample, nitrogen release data (e.g., determined based on the SSURGOdatabase, weather parameters, soil parameters, and/or other moduleinputs or outputs) and nitrogen loss data (e.g., determined based on theSSURGO database, weather parameters, soil parameters, and/or othermodule inputs or outputs) collected over a year can be input intonitrogen change module to determine change in nitrogen in the soil sincean initial nitrogen application (e.g., determined from the totalnitrogen module). Nitrogen change can be for each analysis zone,nitrogen change for an analysis zone relative other analysis zones,and/or for any suitable region.

The system can additionally or alternatively include a nitrogenprescription module, which functions to determine the amount of nitrogento be applied to the crop during side-dress (e.g., after the crop hasgrown beyond a predetermined growth stage). In a first variation, thenitrogen prescription module determines the amount of nitrogen thatshould be supplemented (nitrogen supplement) to achieve to apredetermined crop metric (e.g., size, weight, density, bushel volume,etc.) for each analysis zone. In a second variation, the nitrogenprescription module determines the rate of nitrogen application (e.g.,how much nitrogen should be applied when and/or where) to achieve thenitrogen supplement. In a third variation, the nitrogen prescriptionmodule determines the ideal time for side-dress.

Features and/or inputs used in generating, executing, and or updatingthe nitrogen prescription module can be include any one or more of: thenitrogen change (e.g., nitrogen loss, nitrogen release, etc.), thecurrent available nitrogen (e.g., from the nitrogen change module), thetarget nitrogen amount (e.g., as determined from crop goals), remotemonitoring data, nitrogen stress (e.g., determined from correlation withgreen reflectance of remote monitoring data, etc.), time remaining inthe growing season, the crop growth stage (e.g., current, predicted,historic), crop goals, and/or any other suitable data for the analysiszone. The target nitrogen amount can be: the amount of nitrogen neededto meet a crop goal (e.g., to meet a yield goal, which can be determinedby the crop module or any other suitable module), less the amount ofnitrogen currently available; the initial nitrogen amount (e.g. initialamount of nitrogen applied to the analysis zone); a user-specifiednitrogen amount, and/or be any other suitable nitrogen amount. In aspecific example, the target nitrogen amount can be calculated for eachzone by multiplying the target yield for the zone by a state-specificfactor (e.g., N/bushels). The state-specific factor can be automaticallydetermined (e.g., empirically, selected from a graph, calculated, etc.),specified by a user, calibrated, or otherwise specified. Thestate-specific factor can be determined based on the crop type, cropcultivar, target harvest growth stage, or be determined based on anyother suitable parameter. The target nitrogen amount can be determinedby the nitrogen prescription module, crop module, a user application,and/or any other suitable module.

The nitrogen prescription module can output: the amount of nitrogen thatshould be supplemented (nitrogen supplement) for each analysis zone; avariable nitrogen application rate (e.g., a fixed rate per analysiszone, a variable rate over the analysis zone, management zone, or field,etc.), a fixed nitrogen application rate for the analysis zone,management zone, or field, or output any other suitable information. Inone example, the nitrogen supplement can be the difference between thecurrent nitrogen available and the target nitrogen for the analysiszone. However, the nitrogen supplement can be otherwise determined. Theoutput can be a numerical file, a machine-readable file (e.g., for thenitrogen application equipment), or any other suitable output. When thenitrogen application rate is for the management zone or field, thenitrogen application rate can be optimized across the multiple analysiszones within the management zone or field to minimize cost, maximizeyield, or achieve any other suitable goal.

The nitrogen prescription module can be generated, executed, and/orupdated: in response to receipt of a prescription request for a nitrogenprescription, in response to determination that the farmer shouldside-dress the field, at a predetermined frequency, and/or at anysuitable frequency.

The system can additionally or alternatively include a total nitrogenmodule, which functions to determine the amount of nitrogen available(e.g., at the beginning of a growing season, for a given time point,etc.) within each analysis zone. The determined available nitrogen ispreferably specific to a time or time period, but can be associated withany other suitable time duration. The available nitrogen can bedetermined at and/or for: pre-planting, post-planting (e.g., within apredetermined period post-planting), throughout the growing period(e.g., beginning of the growing period, periodically, in response tooccurrence of a determination event, such as nitrogen prescriptiongeneration, a predetermined growth stage being met, or nitrogenapplication), after harvest, or at any other suitable time. Theavailable nitrogen is preferably associated with the time at which theavailable nitrogen amount is determined, but can be associated with thetime(s) associated with the underlying data from which the availablenitrogen amount is determined, the time associated with the mostup-to-date underlying data from which the available nitrogen amount isdetermined, or associated with any other suitable time. The initialamount of nitrogen (e.g., total amount of nitrogen available at thebeginning of a growing season) is preferably determined after nitrogenapplication to the analysis zone (e.g., nitrogen application to thefield), but can alternatively be determined at any other suitable time.Features and/or inputs used in generating, executing, and or updatingthe total nitrogen module can be include any one or more of: thenitrogen application information for the analysis zone (or themanagement field), the nitrogen history of the analysis zone (e.g., fromprior applications, prior crops, etc.), nitrogen change module output(e.g., nitrogen gain from the nitrogen gain module and/or nitrogen lossfrom the nitrogen loss module, etc.), and/or any other suitable nitrogeninformation. The nitrogen application information, nitrogen history, orother nitrogen information (e.g., soil data, weather data, etc.) arepreferably received from the user account (e.g., from the farmer, fromequipment associated with the user account, etc.), but can alternativelybe otherwise determined. The nitrogen application information caninclude: the time of nitrogen application (e.g., date, hour, etc.), theamount of nitrogen applied (e.g., directly received or indirectlydetermined from the fertilizer type, fertilizer brand, fertilizernitrogen concentration, fertilizer nitrogen type, etc.), the applicationequipment information, or any other suitable information. The totalnitrogen module can output: the total nitrogen available to the cropsfor each analysis zone; the additional nitrogen available to the cropsfor each analysis zone, or any other suitable information. The totalnitrogen module can be generated, executed, and/or updated every growingseason (e.g., after crop harvest, after crop yield is determined, etc.);every time a new nitrogen treatment is performed on the field; and/or atany other suitable frequency.

The system can additionally or alternatively include a management zonemodule (e.g., productivity zone module), which functions to determineanalysis zones for yield analysis, nutrient analysis, nutrientprescription, or any other suitable application. An analysis zone ispreferably a geographic sub-region of a field (e.g., a field belongingto an entity or undergoing substantially the same treatment atsubstantially the same time), but can alternatively be any othersuitable region (e.g., a geographic region, an entire field, etc.). Ananalysis zone is preferably represented in a received image (e.g., asatellite image of a 5 m by 5 m projected geographic region). Receivedimages can partially or fully capture one or more analysis zones,regions related to analysis zones (e.g., soil regions adjacent ananalysis zone, geographic regions surrounding an analysis zone, etc.),and/or any suitable region. An analysis zone can be determined based onany suitable management zone module inputs, such as remote monitoringdata, nitrogen loss or gain (e.g., output from nitrogen change module),growth stage (e.g., output from crop module, etc.), and/or any othersuitable inputs, such as soil zones, as further described below. Anynumber of image elements (e.g., pixels, superpixels, pixel sets orclusters, digital values, image segments, etc.) of a received image canbe mapped to any number (e.g., a plurality) of analysis zones (e.g.,geographic sub-regions in a geographic region). In one variation, eachthe geographic region represented by a pixel in a remote image istreated as an analysis zone. In a second variation, continuousgeographic regions sharing a common parameter set (e.g., crop type, cropgrowth rate, etc.) are treated as an analysis zone. However, theanalysis zones can be otherwise defined. Any number of image elements(e.g., pixels, superpixels, pixel sets, digital values, image segments,etc.) of a received image can be mapped to any number (e.g., a single,plurality) of any suitable region types. Received images are preferablyassociated with a temporal indicator (e.g., a specific time, a recurrenttime, a time period, etc.), but can alternatively be time-agnostic(e.g., a computationally generated reference image, etc.). Additionallyor alternatively, analysis zones can be otherwise represented (e.g., asa set of digital values, coordinates, other location indicators, etc.).Examples of nutrients considered for analysis (e.g., chemical compounds,chemical elements, etc.) can include: nitrogen, nitrogen compounds(e.g., organic nitrogen, inorganic nitrogen, etc.), phosphorous,potassium, limestone, vitamins, minerals (e.g., micronutrients),macromolecules (e.g., proteins), and/or any other suitable chemicalcompound and/or molecule.

The management zone module can additionally function to determinemanagement zones, which are formed from one or more contiguous analysiszones having a substantially uniform yield, vegetative index perreference time (e.g., WDVRI value for June), or sharing any othersuitable yield or crop growth characteristic (e.g., when substantiallythe same management practices have been applied across the analysis zoneand/or the field), but the management zone can be otherwise defined. Themanagement zone module can additionally determine the predicted yield,vegetative index, or other crop parameter value for each analysis zonefor each of a set of reference times, and create a map depicting therelative values of the analysis zones (e.g., yield proxy maps,vegetative index proxy maps, etc.).

These proxy maps can additionally be used to generate seedingprescriptions, wherein a seeding parameter (e.g., seeding rate, seeddensity, etc.) is determined for each analysis zone. In one variation,more seeds are prescribed for higher yield analysis zones, while lessseeds are prescribed for lower yield analysis zones. The seed parameter(e.g., number, deposition rate, etc.) can be determined for each zoneusing a neural network, a rule-based decisionmaking module, selectedfrom graph or chart based on a yield metric (e.g., based on theanticipated yield, yield score, etc.), calculated based on a yieldmetric for the zone, or otherwise determined based on any other suitableparameter. In a second variation, the seed density per analysis zone canbe optimized based on the proxy map. However, the seed prescriptions canbe determined in any other suitable manner. The seeding prescription canbe automatically sent to the user, seeding equipment, or to any othersuitable endpoint.

Features and/or inputs used in generating, executing, and or updatingthe management zone module can be include any one or more of: remotemonitoring data (e.g., satellite images) for the analysis zones recordedover a predetermined duration of time (e.g., 5 years); field boundaries(e.g., a geofence, received from the farmer, extracted from countyrecords, or otherwise determined; coordinates, etc.); soil data;nitrogen loss from a nitrogen loss module; nitrogen gain from a nitrogengain module; other nitrogen change module outputs; growth stage (e.g.,output from crop module, etc.); total nitrogen module outputs (e.g.,available nitrogen at a given time point, etc.) any other suitable inputindicative of historic yield for each of the set of analysis zones; anyinput and/or output of a module of the system; and/or any other suitableinputs. The management zone module can output: analysis zones (e.g.,boundaries, coordinates, pixel identifiers, etc.); proxy maps (e.g.,yield proxy maps, vegetative performance value proxy maps, etc.); anexpected crop parameter value for a given reference time; and/or or anyother suitable information. The management zone module can be generated,executed, and/or updated: in response to receiving an image and/or setof images; in response to a user request (e.g., for nitrogen change, fora nitrogen prescription, for control instructions to control nitrogenapplication equipment, etc.), or at any other suitable time. However,the modules of the system can be otherwise configured.

The system can additionally or alternatively include one or morecomputing systems, which function to generate, execute, and/or updatethe modules, store module-related data (e.g., modules, inputs, outputs,features, etc.) and/or user account data, or perform any other suitablefunctionality. In a first variation, a remote server manages allmodules, and manages the computation for one or more analysis zones,exclusive of other analysis zones (which are handled by other servers).The server can locally store or otherwise access only the data relevantto the analysis zone (e.g., crop stressor data, past yield data, etc.).In a second variation, the computing system can include a cluster ofservers. In a first sub-variant, each server within the cluster canspecialize in a module, exclusive of the other modules. In a secondsub-variant, the computation for all or a subset of the modules can bedistributed across all servers. In a third variation, the set of serversinclude a set of resource servers, user account servers, and analysisservers. These servers are preferably stateless, but can alternativelybe stateful or have any other suitable characterization. The set ofresource servers can store persistent data, such as historic yield data(e.g., WDRVI, LAI, and satellite images); weather data; soil data;nitrogen profiles for different fertilizer types; crop module parametersfor different cultivars; or any other suitable data used for nitrogenmonitoring or prescription determination. The user account servers canstore user account data, such as the geographic locations (e.g.,geographic regions, geographic sub-regions, etc.) with which each useraccount (e.g., farmer) is associated; machines available to the farmer(e.g., seeding machines, nitrogen application machines, otheragricultural equipment etc.); cultivar; yield goals; management data(e.g., planting date, treatment history, etc.); crop history; currentcrop parameters (e.g., as determined by the crop module); or any othersuitable user-associated information. The user account data can bereceived from the user, be automatically determined (e.g., fromsecondary data received from the user or secondary sources, such as thesatellite data), or be otherwise determined. The analysis servers canrun the analysis modules, where the underlying data can be retrievedfrom the resource servers and/or user account servers.

Additionally or alternatively, the computing system can includeprocessing components of a user device (e.g., laptop, desktop, tablet,smartphone, agricultural equipment, etc.), and/or other suitablecomponent. However, computation and storage can be distributed in anyother suitable manner across any suitable components. However, computingsystems can be otherwise configured.

The system can additionally or alternatively include agriculturalequipment, which functions to execute one or more nitrogen prescriptionsand/or control instructions in facilitating nitrogen management for oneor more geographic regions and/or sub-regions. Additionally oralternatively, the agricultural equipment can function to recordcrop-related measurements for use as features and/or inputs ingenerating, executing, and/or calibrating one or more modules.Agricultural equipment can include any one or more of: fertilizer(nitrogen, phosphorous, potassium, etc.) application equipment (e.g.,applicators, stabilizers, spreaders, sprayers, fertigation systems,nurse tanks, etc.), seeding machinery, harvesting equipment (e.g.,combine, reaper, thresher, winnower, etc.), and/or any suitableagricultural equipment. Agricultural equipment preferably operatesaccording to control instructions (e.g., generated by a computing systembased on a nitrogen prescription, etc.), but can additionally oralternatively operate manually in response to user input.

Crop-related measurements can include nitrogen data (e.g., nitrogenapplication, nitrogen availability, nitrogen change, etc.), cropstressor data, and/or any suitable data. In a variation, crop-relatedmeasurements can include sensor data sampled at agricultural equipmentsensors and/or user device sensors, or include any other suitable data.The sensors can include any one or more of: mass flow sensors (e.g.,crop flow sensors, grain flow sensors, etc.), pressure sensors, opticalsensors, camera subsystem, motion sensors, or any other suitable sensor.Crop-related measurements are preferably recorded at agriculturalequipment, but can additionally or alternatively be recorded at anotheruser device (e.g., smartphone, tablet, smart watch, etc.), by anon-board system connected to the agricultural equipment, or by any othersuitable system. Crop-related measurements are preferably transmitted toa remote server of the system, but can be otherwise communicated.However, agricultural equipment can be otherwise operated or configured.

4. Method.

As shown in FIGS. 1-3, a method 100 for managing nitrogen within ageographic region includes: determining a growth stage for thegeographic region using a crop module S110; and determining a nitrogenchange for the geographic region, based on the growth stage, using anitrogen change module S120. The method can additionally oralternatively include determining an amount of nitrogen initiallyavailable for the geographic region S122.

As shown in FIG. 1B, the method 100 can additionally or alternativelyinclude determining a nitrogen prescription for the geographic regionbased on the nitrogen change using a nitrogen prescription module S130;generating control instructions for the nitrogen application equipmentbased on the nitrogen prescription S140; determining an updated module(e.g., an updated crop module, an updated nitrogen prescription module,etc.) S150; identifying a nitrogen status anomaly based on the nitrogenchange S160; and/or presenting nitrogen-related information to a useraccount S170.

As shown in FIG. 2, the method 100 and/or portions of the method 100 canbe performed any number of times at a predetermined frequency (e.g.,every day, week, month, etc.), upon the occurrence of an analysis event(receipt of a remote monitoring datapoint, such as a satellite image ofthe analysis zone; receipt of a nitrogen request from user; etc.),and/or at any suitable time and/or frequency. The method 100 ispreferably performed at a remote server and/or other computing system,but portions of the method 100 can be performed at any suitablecomponent.

As shown in FIGS. 1-4 and 6, determining a growth stage for thegeographic region using a crop module Silo functions to determine thephenology and/or nutrient requirements for the crops within an analysiszone for a given time (e.g., historic, current, predicted crop growthstage). The crop growth stage can be determined (e.g., estimated,calculated, etc.) for each crop within one or more analysis zones (e.g.,each geographic sub-region of a plurality of geographic sub-regions),for a model crop representative of the crops within an analysis zone,for any suitable number of actual, physical crops within one or moreanalysis zones. The crop growth stage is preferably determined using thecrop module, but can alternatively be received from the user (e.g.,where the user physically visits one or more analysis zones and recordsthe growth stage), and/or be otherwise determined. Similar or differencecrop modules can be generated, executed, and/or calibrated for differentcrops, analysis zones, and/or any suitable component. In anotherexample, the crop module is run for the field as whole, using theaverage, mean, and/or other aggregate measure of the seed, weather,soil, nitrogen, and/or other data.

Determining the growth stage is preferably based on a crop module andone or more received images. For example, a vegetative performance valuedetermined based on one or more received images can be used indetermining whether a crop is within a vegetative phase and/orreproductive phase, and a corresponding growth phase model can be usedin predicting growth stage. In a specific example, determining thegrowth stage can include extracting a vegetative performance value forthe geographic region from a received image; selecting a growth curvefor the geographic region based on the vegetative performance value; anddetermining a transition date based on the growth curve; selecting agrowth phase model (e.g., vegetative phase model, reproductive phasemodel, etc.) based on a current date and the transition date; anddetermining the growth stage based on the growth phase model.Additionally or alternatively determining the growth stage can be basedon crop stressor data (e.g., without using image data) and/or any othersuitable inputs and/or features. However, determining the growth stagecan be performed in any manner analogous to that described in U.S.application Ser. No. 15/012,749 filed 1 Feb. 2016, and/or otherwiseperformed.

As shown in FIGS. 1-4 and 6, determining a nitrogen change for thegeographic region using a nitrogen change module S120 functions todetermine how much nitrogen has been lost (e.g., from crop uptake,rainfall, etc.) or gained (e.g., from soil release, etc.) over time(e.g., from initial nitrogen application to a given point in time) forone or more analysis zones. The nitrogen change for the geographicregion is preferably determined based on the growth stage determined inS110, but can be otherwise determined.

The nitrogen change is preferably determined by the nitrogen changemodule, but can alternatively be determined by any other suitablemodule. Determining nitrogen change can be for any number of zones,using any number of nitrogen change modules. Additionally oralternatively, determining nitrogen change can be on a per-nitrogenfertilizer type basis (e.g., change of a first nitrogen fertilizer type,change of a second nitrogen fertilizer type), per-crop type basis,and/or determined along any suitable dimension. Determining nitrogenchange can be performed in response to occurrence of an analysis event(e.g., determining a growth stage using the crop module; identifying anitrogen status anomaly; receiving an image of the zone or region;etc.), at a predetermined frequency, and/or at any suitable time.

Determining nitrogen change is preferably based on growth stage forplants within one or more analysis zones, but can be otherwisedetermined. In a variation, the consumed nitrogen can be the nitrogenuptake required for the plant to get to the determined growth stage(e.g., a current growth stage, a future growth stage) from a historictime (e.g., a time of initial nitrogen application) and/or historicgrowth stage (e.g., determined and/or predicted by the crop module). Thenitrogen uptake for one or more growth stages can be retrieved from aresource database (e.g., based on the growth stage, associatedphenology, etc.), calculated (e.g., using the crop module), or otherwisedetermined. The nitrogen uptake required for the crop to grow from theprevious growth stage to the current growth stage can additionally oralternatively account for crop stressor data during the analysis period,where the crop stressor nitrogen uptake can determined and added to thehistoric stage-to-stage nitrogen uptake for the crop. However, thenitrogen uptake for the crop can be otherwise determined.

Additionally or alternatively, determining nitrogen change can be basedon environmental effects (e.g., due to crop stressors, denitirification,leach, runoff, volatilization, conditions converting nitrogen to other,crop-inaccessible nitrogen compounds, etc.) Determining the nitrogenchange due to environmental effects can include: determining cropstressor values and modeling the nitrogen change based on the cropstressor values. For example, the nitrogen uptake required for the cropto grow from the previous growth stage to the current growth stage canadditionally account for crop stressor data during the analysis period,where the crop stressor nitrogen uptake can determined and added to thehistoric stage-to-stage nitrogen uptake for the crop, but the nitrogenuptake for the crop can be otherwise determined. The crop stressor datathat are determined are preferably measurements recorded (e.g., byagricultural equipment, a third party, a user device, etc.) during theintervening time period (e.g., from the initial nitrogen applicationtime till the given time; from the given time for the last nitrogenchange analysis to the current time; etc.), but can alternatively be anyother suitable data.

As shown in FIGS. 1A and 2-3, the method can additionally oralternatively include determining an amount of nitrogen initiallyavailable S122, which functions to determine a starting value fornitrogen change determination. Nitrogen initially available can bedetermined for each zone (e.g., analysis zone, management zone), for thefield as a whole, and/or any suitable region. The amount of nitrogeninitially available is preferably determined based on the amount ofapplied nitrogen, and can additionally be determined based on thenitrogen history of one or more zones (e.g., based on the past cropsgrow; based on historic nitrogen prescriptions applied; etc.) or bedetermined based historical user input, data feeds, amount of appliednitrogen, features and/or inputs mentioned above, or based on any othersuitable information. The amount of nitrogen initially available can bedetermined by the total nitrogen module, and/or any other module. Theamount of nitrogen initially available within each analysis zone ispreferably determined upon receipt of the nitrogen applicationinformation, but can alternatively be performed at any other suitabletime.

As shown in FIG. 1A, the method can additionally or alternativelyinclude receiving information indicative of nitrogen application for oneor more zones S124. The information can be used to determine a startingdate for nitrogen change determination, be used to determine the amountof nitrogen initially available S122, or be otherwise used. Theinformation indicative of nitrogen application can include: the nitrogenapplication date, fertilizer type, fertilizer amount, fertilizerdensity, fertilizer application rate, fertilizer brand, fertilizevolume, other indirect nitrogen indicator (e.g., received in lieu of anitrogen mass measure), the prior crop in the zone, the planting date,cultivar, seed distribution across the zone (e.g., row spacing, seedrate and one or more seeding implement locations, etc.), historicnitrogen prescriptions, historic control instructions, crop goals (e.g.,yield, number bushels, mass, crop shape, crop morphology uniformity,etc.), side-dress dates, crop-related measurements (e.g., sensormeasurements) from agricultural equipment, nitrogen applicationequipment parameters (e.g., variable rate treatment capabilities,maximum and minimum application rates, maximum and minimum traversalrates, etc.), images of the zone, vegetative performance values (e.g.,WDRVI, LAI, etc.), automatically transmitted to a computing system byequipment associated with the user account (e.g., the seeding equipment,fertilizer application equipment, user devices such as a smart phone,etc.), and/or any other suitable information. The information can bereceived from the user (e.g., in response to a query), retrieved from adatabase (e.g., based on a data identifier, such as a nitrogenapplication equipment identifier, fertilizer identifier, etc.), receivedby a third party, and/or otherwise determined. In a first example, themethod 100 can include receiving sensor measurements of the nitrogenapplication equipment, the sensor measurements sampled during a historictime; automatically determining an initial nitrogen amount for thegeographic region at the historic time based on the sensor measurements(e.g., from mapping sensor values to nitrogen amount; from using amachine learning model with sensor measurements and supplemental datasuch as crop stressor data; etc.); and determining the nitrogen changebetween the historic time to the first time based on the initialnitrogen amount. In a second example, the method 100 can includereceiving a first image of the geographic region, the first imagecorresponding to a first time (e.g., prior to planting); extracting oneor more vegetative performance values for the geographic region; anddetermining an amount of nitrogen initially available for the geographicregion based on the vegetative performance value (e.g., by evaluatingweed growth in the geographic region at the first time). In a thirdexample, determining the amount of nitrogen can include calculating theamount of nitrogen initially applied based on the concentration ofinorganic nitrogen within the fertilizer (e.g., retrieved from theresource server set based on the fertilizer identifier) and the volumeof fertilizer applied (e.g., where the rate is received from theequipment or the volume is entered by the farmer, etc.). In a fourthexample, determining nitrogen initially available can include modelingfertilizer runoff based on terrain (e.g., relative positions of theanalysis zones), modeling fertilizer transport based on environmentalfactors during nitrogen application (e.g., wind, rain, etc.), and/orotherwise accounting for initial fertilizer travel.

In a first variation, determining the amount of nitrogen initiallyavailable includes dividing the amount of nitrogen applied to the fieldby the number of analysis zones. In a second variation, determining theamount of nitrogen initially available in each analysis zone includesdividing the total amount of fertilizer applied to the field by a unitarea (e.g., 1 meter squared). In a third variation, determining theamount of nitrogen initially available in each analysis zone can includecalculating the amount of nitrogen applied to each analysis zone, basedon the estimated time at which the fertilizing apparatus was collocatedwith the analysis zone and the fertilizer application rate at that time.In a fourth variation, determining the amount of nitrogen initiallyavailable in each analysis zone can include calculating the amount ofnitrogen applied to each analysis zone, based on the nitrogenapplication rate, recorded by the fertilizing apparatus, at one or moregeographic locations within the analysis zone. However, the amount ofnitrogen initially available in each analysis zone can be otherwisedetermined.

As shown in FIGS. 1-4 and 6, the method 100 can additionally oralternatively include determining a nitrogen prescription S130, whichfunctions to determine the amount of nitrogen to apply to one or morezones. Determining a nitrogen prescription can additionally oralternatively include determining the nitrogen requirements to achieve acrop goal S132 and/or determining a nitrogen application time S134. Thenitrogen prescription is preferably tailored to optimize the yield, butcan alternatively or additionally be tailored to obtain a different cropgoal (e.g., crop uniformity such as homogenizing yield across the field,minimize nitrogen waste, volume, individual crop size, minimize cost,target crop growth stage, target crop parameters, etc.) and/or beotherwise optimized. The nitrogen prescription can be used for: initialnitrogen application, side-dressing (e.g., nitrogen application during acrop growth stage between VE to R6), or at any other suitable time. Thenitrogen prescription is preferably determined using the nitrogenprescription module, but can alternatively determined by any othersuitable module. Nitrogen prescriptions (e.g., including nitrogenrequirements for achieving crop goals, nitrogen amount, applicationrate, etc.) can be determined (e.g., using the nitrogen prescriptionmodule) for any number of zones (e.g., as shown in FIG. 8). For example,determining nitrogen prescriptions (e.g., with a single nitrogenprescription module) can be for each geographic sub-region of aplurality (e.g., based on nitrogen change for each geographicsub-region). The nitrogen prescription is preferably determined uponreceipt of a prescription request from a user, but can alternatively bedetermined at any other suitable time (e.g., at a date prior toscheduled nitrogen application; in response to determining a nitrogenchange; etc.).

In a first variation, determining the nitrogen prescription can be basedon a target yield (e.g., a yield goal). In a first example, determiningnitrogen prescription can include determining the nitrogen prescriptionbased on predicted yield data and target yield data for the geographicregion at a future time. In a specific example where the geographicregion includes a plurality of geographic sub-regions, the predictedyield data can include predicted yields for each geographic sub-regionof the plurality, the target yield data can include target yields foreach geographic sub-region of the plurality, and determining thenitrogen prescription can include determining sub-region nitrogenprescriptions for each geographic sub-region of the plurality based ondifferences between the predicted yields and the target yields. In asecond example, the method 100 can include: receiving a yield goal forthe field (e.g., 300 bushels of substantially uniform crop sizedistribution from this field), determining the distribution of targetcrop parameters across the analysis zones or management zones of thefield (e.g., the first analysis zone should yield to bushels, while asecond analysis zone should yield 2 bushels; determined based on thehistoric yield maps, based on an optimization analysis, etc.),determining the target crop growth stage or target crop parameters foreach analysis zone at an estimated, future harvest time (e.g., receivedfrom the user, estimated, etc.), determining the current crop growthstage or current crop parameters for each analysis zone (e.g., based onrecent satellite images, the crop module, etc.), and determining themass of nitrogen supplement required to grow the corn from the currentcrop growth stage or crop parameters to the target crop growth stage ortarget crop parameters for each analysis zone. In a third example, themethod 100 can include: receiving a yield goal for the field (e.g.,1,000 kg from this field), determining a target crop distribution acrossthe analysis zones or management zones of the field, determining acurrent crop growth stage or current crop parameters for each analysiszone, determining the target crop growth stage or target crop parametersfor each analysis zone at an estimated, future harvest time, based on acost minimization analysis, and determining the nitrogen supplement massrequired to grow the corn from the current crop growth stage or cropparameters to the target crop growth stage or target crop parameters.

In a second variation, determining the nitrogen prescription includes:applying more nitrogen to areas historically having higher yield (e.g.,based on the yield proxy map), and applying less nitrogen (e.g., theminimum amount required to meet the crop goal) to areas historicallyhaving lower yield.

In a third variation, determining the nitrogen prescription includes:applying more nitrogen to areas estimated to have below-historic yieldpotential (e.g., based on the satellite images, crop module, etc.).

In a fourth variation, determining the nitrogen prescription includes:determining the initial nitrogen amount for each analysis zone,determining the amount of nitrogen left in each analysis zone, anddetermining the amount of nitrogen needed in each analysis zone toregain the initial nitrogen amount.

In a fifth variation, determining the nitrogen prescription can be basedon user-selected preferences (e.g., types of nitrogen, supply ofnitrogen, preferred range of amount of nitrogen application, preferredtimes of nitrogen application, etc.). For example, a user can input anamount of nitrogen and/or amount of money available, and a nitrogenprescription can be generated in accordance with the user-inputtedlimitations. However, the nitrogen prescription can be otherwisedetermined.

The nitrogen prescription can be: a nitrogen mass, a nitrogenapplication rate for the field (e.g., a constant nitrogen applicationrate), a nitrogen application rate for each analysis zone or managementzone (e.g., a variable nitrogen application rate), nitrogen applicationfor different types of nitrogen fertilizer, and/or be any other suitablemeasure of nitrogen mass or derivative thereof.

In a first variation, a nitrogen mass is determined for the entirefield. In this variation, the nitrogen supplement masses for eachconstituent analysis zone within the field can be summed to obtain thenitrogen mass. However, the nitrogen mass for the field can be otherwisedetermined.

In a second variation, a constant nitrogen application rate isdetermined for the entire field. In one embodiment, the constantnitrogen application rate can be determined by dividing the nitrogenmass for the field (e.g., determined in the first variation or otherwisedetermined) by the equipment traversal distance. In a second embodiment,the nitrogen application rate for each analysis zone is determined, andthe average or mean nitrogen application rate for the population ofanalysis zones is determined as the constant nitrogen application rate.In a third embodiment, the constant nitrogen application rate is anoptimized rate based on the nitrogen supplement masses for each analysiszone or management zone within the field. However, the constant nitrogenapplication rate can be otherwise determined.

In a third variation, a variable nitrogen application rate is determinedfor the field, based on the respective nitrogen application rate or massfor each analysis zone or management zone within the field. In thisvariation, a different nitrogen application rate, determined based onthe respective nitrogen supplement mass, is determined for each analysiszone. The nitrogen application rate can then be associated with: ageographic location associated with the analysis zone (e.g., within theanalysis zone, immediately before the analysis zone to accommodate foractuation delay, etc.), an equipment traversal distance (e.g., firstrate for the first 5 m; second rate for next 10 m), an equipmenttraversal rate, duration, and/or path (e.g., first rate for to minuteswhile operating the vehicle at 5 m/min; second rate for 20 minutes whileoperating the vehicle at 10 m/min), or otherwise associated with adirect or indirect geographic measure.

As shown in FIG. 1B, determining a nitrogen prescription canadditionally or alternatively include determining the nitrogenrequirements to achieve a crop goal (e.g., received by a user) S132,which can function to identify the nitrogen supplement to apply to oneor more zones in obtaining one or more crop goals. In a first variation,the crop module (e.g., the most up-to-date crop module) and/or othermodule can be used to automatically estimate the nitrogen requirementsto achieve the crop goal (e.g., target crop growth stage, target cropparameters). In this variation, crop stressors (e.g., weatherparameters, soil parameters, etc.) can be forecasted (e.g., usingmachine learning modules, etc.) and/or otherwise determined for use indetermining nitrogen requirements. In a second variation, the nitrogenrequirements for the target crop growth stage, target crop parameters,and/or other crop goals can be selected or otherwise retrieved from adatabase (e.g., from the resource database), where the nitrogensupplement mass can include the difference in nitrogen requirementsbetween the current growth stage or crop parameters and the targetgrowth stage or crop parameters, in addition to a correction factor(e.g., constant, calculated, etc.) to accommodate for environmentalnitrogen loss. In a third variation, the nitrogen requirements aredirectly received from a user. However, the nitrogen requirements forthe target crop growth stage or crop parameters can be otherwisedetermined.

As shown in FIG. 1B, determining a nitrogen prescription canadditionally or alternatively include determining a nitrogen applicationtime S134, which can function to inform a user on when to apply nitrogento the field (e.g., when to side-dress). The nitrogen application timeis preferably a future time, but can alternatively be a current time orany other suitable time. Determining a nitrogen application time caninclude: determining (e.g., estimating) the nitrogen loss rate for eachanalysis zone, based on the estimated crop growth rate for each analysiszone and estimated (e.g., forecasted) crop stressor values for eachanalysis zone. The nitrogen loss rate can be extrapolated to identifywhen soil nitrogen should be supplemented. Additionally oralternatively, the nitrogen application time can be received from theuser account or otherwise determined.

As shown in FIGS. 1-4 and 6, the method 100 can additionally oralternatively include generating control instructions for agriculturalequipment S140 (e.g., fertilizer application equipment such as nitrogenapplication equipment, seeding machinery, harvesting equipment, etc.),which functions to generate instructions for controlling agriculturalequipment to apply and/or facilitate application of nitrogen (e.g.,according to one or more nitrogen prescriptions). The controlinstructions can include instructions for equipment movement,orientation, nitrogen application timing, amount, rate, and/or anysuitable parameter and/or operation of agricultural equipment and/orassociated devices. Determining control instructions is preferably basedon a nitrogen prescription (e.g., control instructions direct theagricultural equipment to apply nitrogen according to parameters of thenitrogen prescription, etc.), but can be based on any outputs of anymodules (e.g., nitrogen change), crop goals, crop-related measurements,and/or any other suitable data.

Determining control instructions can be performed in response tooccurrence of an analysis event (e.g., generating a nitrogenprescription; a scheduled time prior to nitrogen application; userrequest for a prescription and/or control instructions; automaticrequest from the agricultural equipment, etc.), at a predeterminedfrequency (e.g., according to a fertilizer application schedule), and/orat any suitable time. Control instructions can be transmitted directlyfrom a remote server to the agricultural equipment, to a user device(e.g., which can subsequently transmit the control instructions toagricultural equipment; which can present the control instructions to auser to manually implement with agricultural equipment, etc.). However,determining and/or transmitting control instructions can be performed inany suitable manner.

As shown in FIGS. 1-3 and 5-6, the method 100 can additionally oralternatively include determining an updated (e.g., calibrated) moduleS150, which functions to refine one or more modules in achieving moreaccurate outputs, faster execution (e.g., determination of outputs),faster retrieval of modules, and/or other suitable purposes. Determiningan updated module can be based on one or more actual and/or predicted(e.g., expected): vegetative performance values (e.g., LAI), remotemonitoring imagery (e.g., historic satellite imagery, current satelliteimagery), maps (e.g., yield maps, nitrogen prescription maps, etc.),inputs and/or outputs of a module, and/or any suitable data. Determiningan updated module can be performed for any number and/or type of modules(e.g., crop module, nitrogen change module, nitrogen prescriptionmodule, total nitrogen module, management zone module, calibrationmodules for any of the preceding modules, etc.), and any approachesusable in updating a given module (e.g., crop module) can be usable inupdating a different module (e.g., nitrogen change module, etc.).Determining an updated module and associated portions of the method 100are preferably iteratively performable and/or can be performed anynumber of times. For example, the method 100 can include: determiningnitrogen change and/or a nitrogen prescription based on a first growthstage determined by a first crop module with a first image of thegeographic region; receiving a second image of the geographic region;determining a second crop module (e.g., calibrating the first cropmodule) based on the second image; receiving a third image of thegeographic region; and determining a second growth stage based on thethird image using the second crop module. In another example, the method100 can include repeating any suitable portions and/or steps of themethod 100 using updated modules with same or different (e.g., newer)inputs. Alternatively, determining an updated module can be performedonce. However, determining an updated module can be performed in anysuitable manner.

Determining an updated module can be in response to occurrence of ananalysis event (e.g., verification failure such as actual parameter vs.predicted output parameter from a module differing beyond a threshold;receiving a new remote monitoring datapoint (e.g., satellite image);receiving ground-truth data such as from agricultural equipment and/oruser inputs; a user request such as a prescription request; etc.), at apredetermined frequency, and/or at any suitable time. In an example, acombination of modules (e.g., all system modules) can be updated inresponse to an analysis event being met for a single module (e.g.,actual vs. predicted output for the crop module differing beyond athreshold, etc.).

Determining the updated crop module can include updating moduleequations, input types, features, considered factors, weights, and/orsub-modules; and/or otherwise adjusting the module. For example,determining an updated module can include through different permutationsof the module until conditions for verification are satisfied (e.g.,actual LAI substantially matches predicted LAI from a crop module,etc.). However, any suitable portion of a module can be updated.

Determining an updated module can additionally or alternatively includedetermining an updated crop module S152. The crop module can becalibrated in relation to an analysis event (e.g., before, after, orduring crop growth stage determination; when nitrogen loss to cropuptake is determined; prior to calibrating a nitrogen prescriptionmodule, etc.) and/or at any suitable time. In a first variation,calibrating the crop module can be based on historic remote monitoringdata (e.g., historic satellite imagery). In an example, determining anupdated crop module can be based on a reference yield map for thegeographic region. The reference yield map determined based on a set ofhistoric images corresponding to historic instances of a recurrent timeand/or any other suitable data. Values from the reference yield map canbe compared to one or more outputs of the crop module and/or othermodules. Additionally or alternatively, reference yield map can be usedin calibrating any suitable module. However, calibrating the crop modulecan incorporate any approaches described in U.S. application Ser. No.14/929,055 filed 30 Oct. 2015, which is hereby incorporated in itsentirety by this reference.

In a second variation, calibrating the crop module can be based onvegetative performance values (e.g., WDRVI, LAI, etc.). Determiningvegetative performance values in calibrating the crop module can be forany number of zones (e.g., where differences in actual vs. predictedvegetative performance values for geographic sub-regions can beaggregated and compared to a threshold aggregate difference). In a firstexample, determining an updated crop module can include generating aplurality of sub-region differences by calculating a sub-regiondifference between a sub-region actual LAI and a sub-region anticipatedLAI for each geographic sub-region of a plurality; summing the pluralityof sub-region differences; and comparing the sum to a thresholddifference. In a second example, the method 100 can include receiving aremote monitoring datapoint captured at a first timestamp (e.g., asatellite image), determining a vegetative index (e.g., WDRVI) from thedatapoint (e.g., from the satellite image colors), determining ameasured LAI for groundtruth from the vegetative index; determining acalculated LAI using the crop module, based on crop stressor data up tothe first timestamp; comparing the measured and calculated LAI area; andcalibrating the crop module in response to the calculated and measuredLAI differing more than a threshold amount. In a third example, themethod 100 can include receiving an image (e.g., a first image used indetermining a nitrogen prescription; a second image distinct from thefirst image; etc.) of the geographic region, the image corresponding toa time; determining an actual LAI (e.g., determined from a wide dynamicrange vegetation index (WDRVI) value extracted from the image) for thegeographic region based on the image; determining an anticipated LAI forthe geographic region for the second time based on the first crop moduleand crop stressor data (e.g., weather parameter associated with thetime, soil parameters, historic LAI extracted from an imagecorresponding to a historic time, etc.) sampled prior to the time; anddetermining a second crop module based on the actual LAI and theanticipated LAI. In a fourth example, the calculated LAI can be checkedagainst on-the-ground measurements (e.g., received from the useraccount, equipment, etc.). In a fifth example, calibration can be inresponse to a difference between a predicted growth curve (e.g.,predicted vegetative performance values over times) and an actual growthcurve (e.g., determined based from satellite images, crop-relatedmeasurements from agricultural equipment, etc.). However, the cropmodule can be otherwise calibrated based on vegetative performancevalues.

In a third variation, updating the crop module can be based on growthstage (e.g., comparing predicted vs. actual growth stage). In anexample, the method 100 can include determining a predicted growth stagefor the geographic region at a time, using the crop module (e.g., withcrop stressor data); determining an actual growth stage for thegeographic region (e.g., manually with the leaf collar method,automatically from an image of the crop); and updating the crop modulebased on a difference between the predicted and actual growth stages.

In a fourth variation, updating the module can be based on a timedifference between a predicted transition point (e.g., based on one ormore remote monitoring datapoints preceding the transition point, etc.)and an actual transition point (e.g., based on crop-related measurementsfrom agricultural equipment, based on user input, based on newlyreceived remote monitoring datapoints, etc.) between vegetative andreproductive phases. However, determining an updated crop module can beotherwise performed.

Determining an updated module can additionally or alternatively includedetermining an updated nitrogen change module S154. Calibrating anitrogen change module can be performed in relation to an analysis event(e.g., identifying a nitrogen status anomaly S170; prior to, during,and/or after determining a nitrogen change; etc.), and/or at anysuitable time. In variations, determining an updated nitrogen changemodule can include comparing actual and predicted values for one or moreof: nitrogen change over time (e.g., for one or more zones, fordifferent types of nitrogen fertilizer, for one or more growth stages,etc.); nitrogen availability (e.g., over time, at an instantaneous time,etc.), and/or any other suitable parameters. Actual nitrogen changevalues can be determined from in-situ measurements or otherwisedetermined.

Determining an updated module can additionally or alternatively includedetermining an updated nitrogen prescription module S156. Updating thenitrogen prescription can be in relation to an analysis event (e.g., inresponse to a nitrogen prescription request from a user, after harvest,etc.), and/or at any suitable time. The nitrogen prescription module ispreferably verified based on harvest data associated with the analysiszones, where the estimated crop parameter values for each analysis zone,determined based on the nitrogen prescription, can be compared againstthe actual crop parameter values from the harvest data. The harvest datacan be collected by the combines or other harvesting equipment, enteredby the user, or otherwise determined. The harvest data is preferablyassociated with a geographic identifier (e.g., set of coordinates,analysis zone identifier, etc.), but can alternatively or additionallybe associated with any other suitable set of information. The geographicidentifier can be associated automatically by the system (e.g., based onaccelerometer data, data of harvesting equipment traversal over thefield, etc.), automatically by the harvesting equipment, and/orotherwise associated with a geographic identifier. The geographiclocation associated with the harvest data can additionally be correctedfor traversal delays between harvest and measurement.

In a variation, calibrating the nitrogen prescription module can bebased on actual and predicted yields. For example, calibrating caninclude: determining a predicted yield for the crops based on thenitrogen prescription; determining an actual yield for the geographicregion; and calibrating the nitrogen prescription module (e.g., a statespecific factor for the module) based on a difference between thepredicted yield and the actual yield. In another example, calibratingcan include: determining the actual yield for each analysis zone basedon the respective harvest data associated with geographic identifiersfalling within the analysis zone; determining the estimated yield basedon the nitrogen prescription and crop stressor data up to the harvestdate (e.g., based on the crop module for the harvest date); comparingthe estimated yield with the actual yield; and calibrating the nitrogenprescription module in response to the estimated yield differing fromthe actual yield by a threshold difference (e.g., by iterating throughdifferent model permutations until the estimated yield substantiallymatches the measured yield). Actual yield can be automaticallydetermined from harvest data from harvesting equipment; determined basedon vegetative performance values extracted for images preceding and/orafter a scheduled harvest time; and/or otherwise determined. However,the nitrogen prescription module can be otherwise calibrated.

As shown in FIG. 1B, the method 100 can additionally or alternativelyinclude identifying a nitrogen status anomaly (e.g., a nitrogen stress)S160, which functions to identify unexpected nitrogen statuses (e.g.,unexpected nitrogen availability, unexpected nitrogen loss, etc.) of oneor more zones, to catalyze presentation of the nitrogen status anomalyto a user at a user device and/or modification of the nitrogenprescription to accommodate one or more nitrogen status anomalies,and/or for any suitable purpose. Identifying nitrogen status anomalies(e.g., with a nitrogen anomaly module) can be based on crop stressors(e.g., weather parameters, soil parameters, pests, human interference,etc., crop inputs (e.g., changes in fertilizer application, changes inseeding, etc.), and/or any suitable variable. Any number and/or type ofnitrogen status anomalies can be determined for any number of zones.

Identifying a nitrogen status anomaly is preferably in response todetermining a nitrogen change (e.g., nitrogen loss, nitrogen gain,etc.), but can be performed in relation to any analysis event (e.g.,determining a growth stage; identifying a crop health anomaly;responding to a user request for nitrogen status), and/or at anysuitable time. Identification of nitrogen status anomalies can triggerperformance of any suitable portion of the method 100 (e.g., generatinga nitrogen prescription; determining an updated module, etc.).

In variations, identifying a nitrogen status anomaly can be based on oneor more of: weather parameters, soil parameters, a difference in actualnitrogen availability (e.g., determined based on vegetative performancevalues extracted from satellite images; determine for an instance of arecurrent time; over time; etc.) vs. predicted nitrogen availability(e.g., determined with the nitrogen change module; determined with thetotal nitrogen module; determined for a recurrent time; over time;etc.); supplemental data (e.g., unexpected nitrogen availability givencrop stressor data over the preceding month, etc.); a comparison ofnitrogen-related information (e.g., expected differences in nitrogenavailability and/or nitrogen change between zones, which can bedetermined from historic nitrogen-related information, etc.) for one ormore zones (e.g., a geographic sub-region) and one or more associatedzones (e.g., an adjacent geographic sub-region); a difference above athreshold value between an actual and predicted crop parameter (e.g.,vegetative index) post-nitrogen application; and/or any suitablecriteria. However, identifying a nitrogen status anomaly can beotherwise performed.

In a variation, identifying a nitrogen status anomaly can includedetermining a nitrogen stress based on reflectance values associatedwith remote monitoring data. Higher reflectance can be correlated withincreased nitrogen stress, indicating that more nitrogen should be addedto the region of stress. Nitrogen stresses and/or other nitrogen statusanomalies can be an analysis event triggering determination of anitrogen prescription to treat the nitrogen status anomaly. Determiningnitrogen stress based on reflectance preferably includes evaluatingpredetermined wavelength ranges (e.g., 518-572 nm, 520-550 nm, 695-705nm, 690-730 nm, etc.), wavelength ratios, and/or any suitablewavelengths. Wavelength ranges used for evaluation can be determinedbased on growth stage, crop hybrid or variety, and/or other suitablecriteria. Analysis zones represented in the remote monitoring data canbe classified (e.g., as a nitrogen stress region; with a nitrogen stressvalue indicating degree of nitrogen stress; etc.) based on reflectancethresholds (e.g., identifying a nitrogen stress if green reflectanceexceeds a threshold reflectance), supplementary data (e.g., identifyingnitrogen stress based on a combination of high green reflectance andincreased rainfall, etc.), comparisons with historic reflectance values(e.g., identifying a nitrogen stress in response to a reflectance valuefor a current instance of a recurrent time point exceeding an expectedreflectance value determined based on historic instances of therecurrent time point), and/or any suitable criteria. However,identifying nitrogen status anomalies based on reflectance values can beotherwise performed.

Identifying a nitrogen status anomaly can include selecting (e.g.,prioritizing, filtering, etc.) zones to evaluate for nitrogen statusanomalies. Selecting zones can be based on parameters (e.g., soilparameters, weather parameters, etc.) used in identifying a nitrogenstatus anomaly, supply availability (e.g., labor, fertilizer, supplylocation, supply amount, etc.), temporal parameters (e.g., time point inthe growing season, etc.), remote monitoring data (e.g., newly availableremote monitoring data, etc.), and/or any suitable criteria. In anexample, selecting zones can include prioritizing zones for evaluatingnitrogen status based on rainfall for a zone (e.g., 20% above averagerainfall), labor supply availability (e.g., zones within 30 miles ofcontractors who can apply late season nitrogen), and/or remotemonitoring data (e.g., zones with satellite imagery available for theprevious two weeks). In another example, selecting zones can includereceiving a rainfall parameter for the geographic region, the rainfallparameter associated; and in response to the rainfall parameterexceeding a historic rainfall parameter, evaluating nitrogen status forthe geographic region based on the rainfall parameter and a reflectancefor an image of the geographic region (e.g., an image used indetermining growth stage and nitrogen change for estimating nitrogenavailability), where determining a nitrogen prescription is in responseto the nitrogen status indicating a nitrogen status anomaly (e.g., anitrogen stress). However, prioritizing zones to evaluate for nitrogenstatus anomalies can be performed in any suitable manner.

As shown in FIGS. 1, 7, and to, the method 100 can additionally oralternatively include presenting nitrogen-related information (e.g.,nitrogen change, availability, prescriptions, status anomalies, etc.) toa user account (e.g., at a user device) S170, which functions to informa user (e.g., a farmer) of the nitrogen-related information. a metricfor how much nitrogen is left in their fields. Presentingnitrogen-related information can be presented as one or more of: avalue, map (e.g., illustrating geographic sub-region nitrogenprescriptions), machine-readable file (e.g., control instructionsautomatically loaded onto agricultural equipment), notification,graphic, audio, video, and/or any suitable format. However, presentingnitrogen-related information can be otherwise performed.

An alternative embodiment preferably implements the above methods in acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with a nitrogen analysis system. The nitrogenanalysis system can include a field system, crop modeling system,nitrogen change system, and a nitrogen prescription system. Thecomputer-readable medium can be stored on any suitable computer readablemedia such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD orDVD), hard drives, floppy drives, or any suitable device. Thecomputer-executable component is preferably a processor but theinstructions can alternatively or additionally be executed by anysuitable dedicated hardware device.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, where the method processes can beperformed in any suitable order, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for managing nitrogen within a geographic region,the method comprising: a) receiving a first image of the geographicregion, the first image associated with a first time; b) determining afirst growth stage at the first time for plants within the geographicregion based on the first image using a first crop module; c)determining a first nitrogen change for the geographic region based onthe first growth stage using a nitrogen change module; d) estimating afirst nitrogen availability for the geographic region at the first timebased on the first nitrogen change; e) repeating b) to d) for a secondnitrogen availability for the geographic region at a second time basedon a second nitrogen change and the first nitrogen availability; f)determining a nitrogen prescription for the geographic region based onthe second nitrogen availability using a nitrogen prescription module;and g) generating first control instructions based on the nitrogenprescription, wherein nitrogen application equipment applies nitrogenaccording to the first control instructions.